Existing instructional videos datasets [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} majorly consist of third-person videos.
Here, the camera is kept far from the expert, to avoid interference in the actual task.
Due to this, the manipulated objects are typically small or sometimes invisible.
Additionally, third-person videos can be captured from various positions, leading to wide variations in the camera viewpoints for the same task [9]}.
Further, most datasets comprise videos scraped from the internet (YouTube) [2]}, [4]}, [5]}, [7]}, [8]}, which are noisy and have large irrelevant segments.
In contrast, egocentric cameras are typically harnessed to the subject's head and have a standardized location.
They provide a clearer view of the executed task, including the manipulated objects.
As a result, recent works have introduced datasets consisting of egocentric videos [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, which have proven helpful for various tasks [21]}, [22]}, [23]}, [24]}, [25]}.
